Although advanced interatrial block (aIAB) is an established electrocardiographic phenotype, its prevalence, incidence, and prognostic significance in the general population are unclear. We examined the prevalence, incidence, and prognostic significance of aIAB in 14,625 (mean age = 54 ± 5.8 years; 26% black; 55% female) participants from the Atherosclerosis Risk in Communities (ARIC) study. aIAB was detected from digital electrocardiograms recorded during 4 study visits (1987 to 1989, 1990 to 1992, 1993 to 1995, and 1996 to 1998). Risk factors for the development of aIAB were examined using multivariable Poisson regression models with robust variance estimates. Cox regression was used to compute hazard ratios and 95% CIs for the association between aIAB, as a time-dependent variable, and atrial fibrillation (AF). AF was ascertained from study electrocardiogram data, hospital discharge records, and death certificates thorough 2010. A total of 69 participants (0.5%) had aIAB at baseline, and 193 (1.3%) developed aIAB during follow-up. The incidence for aIAB was 2.27 (95% CI 1.97 to 2.61) per 1,000 person-years. Risk factors for aIAB development included age, male gender, white race, antihypertensive medication use, low-density lipoprotein cholesterol, body mass index, and systolic blood pressure. In a Cox regression analysis adjusted for sociodemographics, cardiovascular risk factors, and potential confounders, aIAB was associated with an increased risk for AF (hazard ratio 3.09, 95% CI 2.51 to 3.79). In conclusion, aIAB is not uncommon in the general population. Risk factors for developing aIAB are similar to those for AF, and the presence of aIAB is associated with an increased risk for AF.
Advanced interatrial block (aIAB) exists when a delay of conduction occurs over the Bachmann bundle. It manifests on the 12-lead electrocardiogram (ECG) as a prolonged P wave with biphasic morphology (±) in the inferior leads. Although aIAB is an established electrocardiographic phenotype, its prevalence, incidence, and prognostic significance in the general population are unclear. Previous reports from patient-based populations have shown that aIAB is associated with an increased risk for paroxysmal supraventricular tachyarrhythmias. The presence of aIAB also was shown to be an independent predictor of new-onset atrial fibrillation (AF) in patients with heart failure and in patients with atrial flutter. In addition, aIAB has been associated with a higher risk of AF recurrence after pharmacologic cardioversion to normal sinus rhythm. To date, the association between aIAB and AF has not been examined in prospective population-based studies. Therefore, the purpose of this analysis was to examine the prevalence and incidence of aIAB as well as the association between aIAB and AF in the Atherosclerosis Risk in Communities (ARIC) study, a community-based prospective cohort study.
The ARIC study prospectively enrolled 15,792 community-dwelling men and women aged from 45 to 64 years. Four field centers across the United States (Washington County, Maryland; Forsyth County, North Carolina; Jackson, Mississippi; suburban Minneapolis, Minnesota) recruited participants from 1987 to 1989. Participants returned for 4 follow-up examinations (1990 to 1992, 1993 to 1995, 1996 to 1998, and 2011 to 2013) and continue to be followed through annual telephone calls to ascertain study end points. End points are further ascertained from examination of lists of hospital discharges that include any cardiovascular diagnoses from hospitals in the study communities. The study was approved by the institutional review boards at all participating universities, and all participants provided written informed consent at the time of study enrollment.
For this analysis, we excluded participants with AF (n = 37), those with missing baseline covariates (n = 783), and participants with missing follow-up data (n = 384). In addition, the few ARIC participants with race other than black or white were excluded, including the small number of black participants from Washington County and Minneapolis.
Digital 12-lead ECGs were obtained at baseline and at subsequent follow-up examinations using MAC PC ECG machines (Marquette Electronics, Milwaukee, Wisconsin). All ECGs were inspected for technical errors and adequate quality at the Epidemiology Coordinating and Research Centre at the University of Alberta (Edmonton, Alberta, Canada) during the initial phases of the study and at the Epidemiological Cardiology Research Center at the Wake Forest School of Medicine (Winston-Salem, North Carolina) during later phases. aIAB manifests on the 12-lead ECG with a P-wave duration ≥120 ms and biphasic (positive–negative) morphology in leads II, III, and aVF. Prevalent aIAB was identified using baseline ECG data, and incident aIAB cases were detected during the first 3 follow-up study visits (1990 to 1992, 1993 to 1995, and 1996 to 1998).
Cases of AF were identified from study visit ECGs, review of hospital discharge diagnoses, and death certificates. A cardiologist visually confirmed all AF cases automatically detected from study ECGs. Information on hospitalizations during follow-up was obtained from annual follow-up calls and surveillance of local hospitals, with hospital discharge diagnosis codes collected by trained abstractors. AF during follow-up was defined by International Classification of Diseases, Ninth Revision codes 427.31 or 427.32. AF detected in the same hospitalization as open cardiac surgery were not included because these cases were considered transient.
Age, gender, and race were self-reported. Tobacco use was defined as current or former cigarette smoking. Diabetes was defined as a fasting glucose level ≥126 mg/dl (or nonfasting glucose ≥200 mg/dl), a physician diagnosis of diabetes, or the use of diabetes medications. Systolic blood pressure was obtained from each participant using sphygmomanometers to measure 3 readings in the upright position after 5 minutes of rest. The average of the last 2 blood pressure measurements was used as the final reading. Antihypertensive medication use was self-reported. Body mass index was defined as the weight in kilograms divided by the square of the height in meters. Low-density lipoprotein (LDL) cholesterol levels were computed indirectly using cholesterol values assayed from serum samples obtained at the baseline study visit. Prevalent heart failure was defined as present if participants reported taking heart failure medications or if participants met all 3 of the Gothenburg criteria. Prevalent coronary heart disease was defined by a self-reported history of physician-diagnosed myocardial infarction, coronary artery bypass surgery, coronary angioplasty, or electrocardiographic evidence of myocardial infarction.
Categorical variables are reported as frequency and percentage, whereas continuous variables are recorded as mean ± SD. Baseline characteristics were examined by stratifying participants by the presence of the following: prevalent aIAB, incident aIAB, and no aIAB. Differences between groups were tested using the chi-square test for categorical variables and the analysis of variance procedure for continuous variables.
Kaplan–Meier estimates were used to compute the cumulative incidence of aIAB. Prevalent aIAB cases were excluded in the analysis to examine incident aIAB. Follow-up time was defined as the time between the baseline visit until aIAB development, loss to follow-up, death, or the end of study visit 4 (1996 to 1998). To examine risk factors for aIAB development, we examined the association between baseline characteristics, obtained from study visit 1, and incident aIAB using multivariable Poisson regression with robust variance estimates to compute relative risk and 95% CIs. Models were adjusted as follows: model 1 adjusted for demographics (age, gender, and race) and model 2 adjusted for variables in model 1 plus body mass index, systolic blood pressure, the use of blood pressure–lowering medication, smoking, diabetes, LDL cholesterol, coronary heart disease, and heart failure.
Kaplan–Meier estimates also were used to examine the cumulative incidence of AF by the presence of aIAB as a time-dependent variable. The first 3 follow-up study visits were used to identify more aIAB cases because of the limited number of cases present in the baseline study visit. Follow-up was defined as the time between aIAB detection until AF development, loss to follow-up, death, or end of follow-up (December 31, 2010). The period between the baseline visit and aIAB diagnosis was considered as non-aIAB follow-up. Cox regression was used to compute hazard ratios and 95% CIs for the association between aIAB as a time-dependent variable and AF. Multivariable models were constructed with baseline characteristics, obtained from study visit 1, as follows: model 1 adjusted for age, gender, and race; model 2 adjusted for model 1 covariates plus body mass index, systolic blood pressure, the use of blood pressure–lowering medication, smoking, diabetes, low-density lipoprotein cholesterol, coronary heart disease, and heart failure. Statistical significance was defined as p <0.05. SAS version 9.4 (SAS Institute Inc., Cary, North Carolina) was used for all analyses.
A total of 14,625 (mean age = 54 ± 5.8 years; 26% black; 55% female) participants were included in the final analysis. A total of 69 participants (0.5%) had aIAB at baseline, and 193 (1.3%) cases of aIAB were detected on subsequent study ECGs (mean follow-up = 5.9 years). Baseline characteristics stratified by prevalent aIAB, incident aIAB, and no aIAB are presented in Table 1 . Participants with aIAB were more likely to be older, men, and white and to have diabetes, coronary heart disease, and heart failure compared with those without aIAB. They also were more likely to smoke, to take antihypertensive medications, and to have higher values for body mass index and systolic blood pressure.
|Characteristics||Advanced Interatrial Block||P-value †|
|Age, mean ± SD (years)||59 ± 4.9||57 ± 5.5||54 ± 5.7||<0.0001|
|Male||45 (65%)||121 (63%)||6,377 (44%)||<0.0001|
|Black||23 (33%)||35 (18%)||3,771 (18%)||0.016|
|Smoking||42 (61%)||122 (63%)||8,383 (58%)||0.37|
|Diabetes mellitus||19 (28%)||27 (14%)||1,593 (11%)||<0.0001|
|LDL cholesterol, mean ± SD (mg/dl)||143 ± 35||147 ± 38||137 ± 39||0.47|
|Body mass index, mean ± SD (kg/m 2 )||30 ± 4.8||29 ± 5.5||28 ± 5.3||<0.0001|
|Systolic blood pressure, mean ± SD (mm Hg)||132 ± 23||129 ± 19||121 ± 19||<0.0001|
|Antihypertensive medications||44 (64%)||108 (56%)||4,229 (29%)||<0.0001|
|Coronary heart disease||13 (19%)||18 (9.3%)||658 (4.6%)||<0.0001|
|Heart failure||8 (12%)||18 (9.3%)||630 (4.4%)||<0.0001|
The crude incidence for aIAB was 2.27 (95% CI 1.97 to 2.61) cases per 1,000 person-years. Table 2 lists the unadjusted and adjusted risk factors for aIAB development. As shown, age, male gender, white race, antihypertensive medication use, LDL cholesterol, body mass index, and systolic blood pressure were significantly associated with aIAB development.
|P-value||Adjusted ∗ |
|Age (per 10-year increase)||2.71 (2.09, 3.54)||<0.0001||2.09 (1.57, 2.77)||<0.0001|
|Male||2.08 (1.56, 2.78)||<0.0001||2.11 (1.55, 2.87)||<0.0001|
|Black||0.63 (0.43, 0.90)||0.012||0.48 (0.33, 0.69)||0.0001|
|Smoking||1.22 (0.91, 1.63)||0.18||1.02 (0.76, 1.38)||0.88|
|Diabetes||1.30 (0.87, 1.94)||0.20||0.75 (0.48, 1.16)||0.19|
|LDL cholesterol (per 10 mg/dl increase)||1.06 (1.03, 1.09)||0.0001||1.04 (1.01, 1.08)||0.019|
|Body mass index (per 5 kg/m 2 increase)||1.30 (1.19, 1.43)||<0.0001||1.31 (1.15, 1.48)||<0.0001|
|Systolic blood pressure (per 10 mm Hg increase)||1.21 (1.15, 1.27)||<0.0001||1.14 (1.06, 1.22)||0.0003|
|Antihypertensive medications||2.99 (2.26, 3.97)||<0.0001||2.41 (1.75, 3.30)||<0.0001|
|Coronary heart disease||2.11 (1.31, 3.41)||0.0022||0.91 (0.55, 1.52)||0.73|
|Heart failure||2.21 (1.37, 3.56)||0.0012||1.21 (0.72, 2.03)||0.47|